Key Responsibilities
● LLM Orchestration: Design and deploy sophisticated LLM-based applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel. ● Model Optimization: Lead the fine-tuning of open-source and proprietary models to improve performance, latency, and cost-efficiency. ● Advanced RAG Systems: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines utilizing vector databases like Pinecone, FAISS, Weaviate, or Azure AI Search. ● Scalable Deployment: Containerize services using Docker and deploy via FastAPI and Azure Functions to ensure high availability and low latency. ● Productization: Build and scale enterprise-grade chatbots, copilots, and automated content generation tools using OpenAI/Azure OpenAI and Hugging Face. ● Prompt Engineering: Implement and manage advanced prompt optimization and versioning workflows to enhance model accuracy. ● Monitoring & Evaluation: Establish robust evaluation frameworks (e.g., RAGAS, TruLens) to track model performance, hallucination rates, and drift in production. ● Operationalization: Collaborate with cross-functional teams (Product, DevOps, Data) to identify high-impact use cases and move them from POC to production. ● Best Practices:



Set the standard for GenAI engineering, including model selection criteria, performance tracking, and ethical AI safeguards. Required Skills & Qualifications ● Overall

Experience:
4-7 years of qualified software engineering experience. ● GenAI Domain

Experience:
2 years of dedicated, hands-on experience building GenAI, LLM, and OCR solutions in production environments. ● Programming Mastery: Deep proficiency in Python with expertise in asynchronous programming, typing standards, and clean code principles. ● Generative AI & LLMs: Hands-on experience with commercial APIs (OpenAI GPT-4o, Anthropic Claude, Azure OpenAI) and open-source models (Llama 3, Mistral), along with modern agentic frameworks. ● Vector Infrastructure & Search: Practical experience with hybrid search (dense + sparse), re
- ranking algorithms, metadata filtering, and vector index tuning. ● Backend & Cloud: Strong background in FastAPI, Docker, Git, CI/CD pipelines, and enterprise cloud ecosystems (Azure / AWS). ● Problem Solving: Proven track record of solving hallucination, context window limitations, and data grounding issues in production.

📌 AI Engineer (India)
🏢 Dimensionless Technologies
📍 India